docstr: batch embed sentences
  
  Args:
      sentences: Sentence list to embed
      debug: show debug info
  Returns:
      vectorized sentence list
docstr_tok: ["batch","embed","sentences","Args",":","sentences",":","Sentence","list","to","embed","debug",":","show","debug","info","Returns",":","vectorized","sentence","list"]
code: def embed(
      self, sentences: List[List[str]], *, debug: bool = False
  ) -> np.ndarray:
      """
      batch embed sentences
  
      Args:
          sentences: Sentence list to embed
          debug: show debug info
      Returns:
          vectorized sentence list
      """
      if self._text_processor is None:
          raise ValueError(
              "Need to setup the `embedding.setup_text_processor` before calling the embed function."
          )
  
      tensor_x = self._text_processor.transform(
          sentences, segment=self.segment, seq_length=self.max_position
      )
      if debug:
          logger.debug(f"sentence tensor: {tensor_x}")
      embed_results = self.embed_model.predict(tensor_x)
      return embed_results
  
file_path: Kashgari/kashgari/embeddings/abc_embedding.py
imports: import json
  from typing import Dict, List, Any, Optional, Union
  import numpy
  import tensorflow
  import tqdm
  import kashgari
  from kashgari.generators import CorpusGenerator
  from kashgari.logger import logger
  from kashgari.processors import ABCProcessor
label_desc: Use image node_id 8 for calling the ABCEmbedding obj's underlying member method code with example usage: obj.embed(sentences) and returns: embed_results